<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.</p>
</abstract>

Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is computation-intensive and can require a long training time. SVM is significantly faster than Convolution Neural Network (CNN). However, the SVM has limited its applications in the mid-size dataset as it requires proper tuning. Recently the parameterization of multiple kernels has shown greater flexibility in the characterization of the dataset. Therefore, this paper proposes a sparse coded multi-scale approach to reduce training complexity and tuning of SVM using a non-linear fusion of kernels for large class natural scene classification. The optimum features are obtained by parameterizing the dictionary, Scale Invariant Feature Transform (SIFT) parameters, and fusion of multiple kernels. Experiments were conducted on a large dataset to examine the multi-kernel space capability to find distinct features for better classification. The proposed approach founds to be promising than the linear multi-kernel SVM approaches achieving 91.12 % maximum accuracy.

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

Department of Electronics and Communication Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Support vector machine (SVM) techniques and deep learning have been prevalent in object classification for many years. However, deep learning is...

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

Publié en ligne: 22 déc. 2021

Pages: 374 - 380

Reçu: 03 mai 2021

DOI: https://doi.org/10.2478/jee-2021-0053

Mots clés
multiple kernel learning, support vector machine, classification, SIFT, spatial pyramid matching (SPM)

© 2021 Bhavinkumar Gajjar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Sparse coded spatial pyramid matching and multi-kernel integrated SVM for non-linear scene classification

Publié en ligne: 22 déc. 2021

Pages: 374 - 380

Reçu: 03 mai 2021

DOI: https://doi.org/10.2478/jee-2021-0053

Mots clésmultiple kernel learning, support vector machine, classification, SIFT, spatial pyramid matching (SPM)

© 2021 Bhavinkumar Gajjar et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
multiple kernel learning, support vector machine, classification, SIFT, spatial pyramid matching (SPM)